{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numbers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"./data/hai_train.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 289/289 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "list = []\n",
    "list2 = []\n",
    "with open(path, 'r', encoding=\"utf8\") as f:\n",
    "    data = f.readlines()\n",
    "for line in tqdm(data):\n",
    "    wav_file, pny, han = line.split('\\t')\n",
    "    list.append(wav_file)\n",
    "    list2.append(han)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/searchliangcheng/searchliangcheng_152.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/searchzhoujielunsong/searchzhoujielunsong_137.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/xiayishou/xiayishou_90.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/jixubofang/jixubofang_39.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/searchliangcheng/searchliangcheng_60.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/bofangyingyue/bofangyinyue_0.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/guanbiyinxiang/guanbiyinxiang_167.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/xiayishou/xiayishou_6.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/jixubofang/jixubofang_49.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/bofangyingyue/bofangyinyue_81231.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/bofangyingyue/bofangyinyue_156.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/searchdengzhiqisong/searchdengzhiqisong_65.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/bofangyingyue/bofangyinyue_47.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/xiayishou/xiayishou_0.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/jixubofang/jixubofang_182.wav',\n",
       " 'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/searchqilixiang/searchqilixiang_183.wav']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "fwavfile = list[-6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'E:/DeepLearning/dl_studio/bishe/my_ch_speech_recognition-master/data/haiwav/bofangyingyue/bofangyinyue_156.wav'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fwavfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io.wavfile as wav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x191b9f640c8>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "fs, data = wav.read(fwavfile)\n",
    "fig, ax = plt.subplots(figsize=(20,4))\n",
    "\n",
    "ax.plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"]=False\n",
    "# for item in list:\n",
    "#     fs, data = wav.read(item)\n",
    "#     fig, ax = plt.subplots(figsize=(20,4))\n",
    "#     ax.set_title(list2[list.index(item)]+\",\"+str(item))\n",
    "#     ax.plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16000\n",
      "[ 0.00000000e+00  0.00000000e+00 -3.05175781e-05 ...  1.03759766e-02\n",
      "  8.11767578e-03  5.18798828e-03]\n"
     ]
    }
   ],
   "source": [
    "def fftnoise(f):\n",
    "    f = np.array(f, dtype=\"complex\")\n",
    "    Np = (len(f) - 1) // 2\n",
    "    phases = np.random.rand(Np) * 2 * np.pi\n",
    "    phases = np.cos(phases) + 1j * np.sin(phases)\n",
    "    f[1 : Np + 1] *= phases\n",
    "    f[-1 : -1 - Np : -1] = np.conj(f[1 : Np + 1])\n",
    "    return np.fft.ifft(f).real\n",
    " \n",
    " \n",
    "def band_limited_noise(min_freq, max_freq, samples=1024, samplerate=1):\n",
    "    freqs = np.abs(np.fft.fftfreq(samples, 1 / samplerate))\n",
    "    f = np.zeros(samples)\n",
    "    f[np.logical_and(freqs >= min_freq, freqs <= max_freq)] = 1\n",
    "    return fftnoise(f)\n",
    "noise_len = 12 # seconds\n",
    "print(fs)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1,ncols=1, figsize=(20,4))\n",
    "plt.plot(data, color='black')\n",
    "ax.set_xlim((0, len(output)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.fft as nf\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1910a967ec8>]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffts = nf.fft(data)\n",
    "sigs7 = nf.ifft(ffts).real\n",
    "plt.plot(sigs7, label=r'$\\omega$='+str(round(1 / (2 * np.pi),3)), alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16000 (128000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1914d7094c8>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_rate, noised_sigs = wav.read(fwavfile)\n",
    "print(sample_rate, noised_sigs.shape)\n",
    "times = np.arange(noised_sigs.size) / sample_rate\n",
    "\n",
    "plt.figure('Filter', facecolor='lightgray')\n",
    "plt.subplot(221)\n",
    "plt.title('Time Domain', fontsize=16)\n",
    "plt.ylabel('Signal', fontsize=12)\n",
    "plt.tick_params(labelsize=10)\n",
    "plt.grid(linestyle=':')\n",
    "plt.plot(times[:178], noised_sigs[:178],c='orangered', label='Noised')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x191c53bb388>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#　傅里叶变换后，绘制频域图像\n",
    "freqs = nf.fftfreq(times.size, times[1]-times[0])\n",
    "complex_array = nf.fft(noised_sigs)\n",
    "pows = np.abs(complex_array)\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.title('Frequency Domain', fontsize=16)\n",
    "plt.ylabel('Power', fontsize=12)\n",
    "plt.tick_params(labelsize=10)\n",
    "plt.grid(linestyle=':')\n",
    "plt.semilogy(freqs[freqs>0], pows[freqs>0], c='limegreen',label='Noised')\n",
    "plt.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1913711f448>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 寻找能量最大的频率值\n",
    "fund_freq = freqs[pows.argmax()]\n",
    "# where函数寻找那些需要抹掉的复数的索引\n",
    "noised_indices = np.where(freqs != fund_freq)\n",
    "# 复制一个复数数组的副本，避免污染原始数据\n",
    "filter_complex_array = complex_array.copy()\n",
    "filter_complex_array[noised_indices] = 0\n",
    "filter_pows = np.abs(filter_complex_array)\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.xlabel('Frequency', fontsize=12)\n",
    "plt.ylabel('Power', fontsize=12)\n",
    "plt.tick_params(labelsize=10)\n",
    "plt.grid(linestyle=':')\n",
    "plt.plot(freqs[freqs >= 0], filter_pows[freqs >= 0],c='dodgerblue', label='Filter')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1918adfb048>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_sigs = nf.ifft(filter_complex_array).real\n",
    "plt.subplot(223)\n",
    "plt.xlabel('Time', fontsize=12)\n",
    "plt.ylabel('Signal', fontsize=12)\n",
    "plt.tick_params(labelsize=10)\n",
    "plt.grid(linestyle=':')\n",
    "plt.plot(times[:178], filter_sigs[:178],c='hotpink', label='Filter')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
